PRIOR BUSH FIRE IDENTIFICATION MECHANISM BASED ON MACHINE LEARNING ALGORITHMS

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.13, No.4, July 2022
DOI: 10.5121/ijaia.2022.13405 67
PRIOR BUSH FIRE IDENTIFICATION MECHANISM
BASED ON MACHINE LEARNING ALGORITHMS
Mohammad Mohammad1
, Aleem Mohammed2
and C Atheeq3
1
Senior Lecturer, Melbourne Institute of Technology, Sydney, Australia,
2
IEEE Research Associate, ACS Research Associate, Sydney, Australia,
3
Associate Lecturer in Department of Computer Science,
Gitam Deemed University, Hyderabad, India.
ABSTRACT
Besides causing awful fatalities resulting in deaths and significant resources like many acres of timberland
and dwelling places, forest fires are a significant threat to sound enormous wilderness biologically and
environmentally. Consistently, a considerable number of fires around the globe reason debacles to
different habitats and layouts. The stated matter has been the investigation premium for a significant length
of time; there is a considerable amount of good concentrated on arrangements available for testing or
perhaps ready to be utilized to determine this disadvantage. Woods and actual flames have been severe
issues for quite some time. Presently, there is a wide range of answers for distinguishing woods fires.
Individuals are utilizing sensors to determine the fire. However, this case isn't workable for vast sections of
land woods. This paper discusses another fire-recognition methodology with incremental advancements.
Specifically, we put forward a stage-Artificial Intelligence. The PC innovation strategies for
acknowledgment and whereabouts of smog and fires, in light of the inert photographs or the graphics
captured by the cameras. AI for tracing down the fires. The accuracy relies on the calculations that use
dataset values later divided in various test and train sets, respectively.
KEYWORDS
Accurate, KNN, Random Forest Algorithm, fire, segmentation.
1. INTRODUCTION
Forests protect the planet's natural equilibrium. Tragically, those left as of now contact an
outsized space, making its administration and obstruction exhausting and surprisingly
impractical. The result is overwhelming adversity, frightful detriment to the environmental
factors and climate, and irreversible damage to biology [1]. Animals during bushfires are
impacted numerously in a total count of around a unit of estimation semi-extremely durable
decisive impacts like effects on local climate examples; warming, and extinction of uncommon
types of greenery [2]. Quick and successful locations play a crucial role in the disaster. The fire
incident occurred in Australia's forest in the years 2019 and 2020, informally termed Dark
Summer. Semi-annually a few flames consumed, stretching along the country's southeast; the
critical flames crested through December–January. The fire destroyed around eighteen million
hectares, annihilating over 6,000 structures and killing at least 34 individuals. Animals accepted
almost around 3 billion earthly creatures to eliminate forcefully. Over 475 million creatures were
determined lost with the problem that all creatures might lose life in that fire incident; soon after
it developed, more than a billion biologists worked for the University of Sydney. Public Aviation
and Space Legislation found that the number of dead koalas on the island is more than 25,000.
Specialists from Charles Stuart University in the year 2020 observed that nine smoky rats died
suddenly due to a shortage of respiration due to smog containing particles of PM2.6 arising from

68
bushfires 50km away.
In southeast states, air quality collapsed to dangerous levels at its peak. Almost 80% of citizens
were hit upfront implication by the disaster caused. In Jan 2020, 11,000km across the Pacific to
Chile and Argentina suffered from smoke. Public Aeronautics and Space Administration resulted
in around 306 million CO gas emitted. All through the next calamity, partner air big hauler and a
handful of other helicopters bolted all through the firefighting tasks; the air big hauler disaster
showed the casualties of three teams. Few smoke engines had trapped in calamitous
circumstances that happened straight due to wild disaster, resulting in the death of three
individuals. As per the record, the overall population donated around $500 million to worldwide
associations, well-known individuals, and VIPs for casualty help and life repossession [3]. Fire
impact moved food, clothing, and domestic animal-vehicle feed out of the impacted regions.
Following this, to keep away from the vast, wild spreading of bush fires, it is significant to trace
down flames early to mitigate the proliferation [4]. This way, the environmental elements can be
saved from life-threatening calamities. Henceforth we've come up with a procedure for
identifying fire and smoke from the captured videos by the strategy for picture handling methods
and AI arrangement calculations. The point of its undertaking is to design a plan that does not
give difficulty recognizing blazes and smog through fire-inflicted videos with more precision. At
first, we undertook the KNN mechanism for predicting the fires, and later Random Forest
calculation was adapted to work on the accuracy.
2. METHODOLOGY
2.1. KNN Algorithm
For classification and predictive regression, we used the K-nearest neighbors (KNN) algorithm, a
supervised ML algorithm for the type of predictive problems in the industry. It's primarily used in
sort since it fairs across all parameters evaluated when determining the usability of a technique. It
measures the closest data. Suppose there are two categories, i.e., Category A and Category B, and
we have a new data point x1 so that this data point will lie in which of these categories [5]. To
solve this type of problem, we need a K-NN algorithm. With the help of K-NN, we can quickly
identify the variety or class of a particular dataset. Firstly, we will choose the number of
neighbors to select the k=5 closest to the model. The information in this research is simulated and
classified. Next, we will calculate the Euclidean distance between the data points. The Euclidean
distance is the distance between two points, which we have already studied in geometry.
It is utilized widely in the suggestion framework of online shopping websites like Flipkart and
entertainment websites like movies. More than 35% of Flipkart's income is through the proposal.
It is also used widely in similar themes. A wide band of information in every step has expanded
widely. The reports on the internet have different thoughts, which is an expected idea. To
separate the view from a gathering of words, we have used a KNN algorithm. KNN could be a
passive student because it doesn't have different performances activated through the preparation
data. KNN retains preparation data; enclosed is the learning data assessment; it has proposed at
the time of solicitation.
2.2. Random Forest Algorithm
It is a process that involves growing a variety of trees during the preparation stage. The majority
of trees are chosen by the irregular woodland at an official selection. A choice tree is a tree-
shaped outline that is used to choose an activity's route. Each branch of the tree corresponds to a
probable option activity or event [6]. Assume there are three organic items in a dish, one each of

69
cherry, orange, and apple. We're serious about isolating it. So, initially, we use the criteria
"diameter>=3" to isolate cherry because its breadth is limited. The other fruits are then mixed
together. Later, with the help of other criterion, "Is shading current fruit?". If it is then isolation is
done for orange and passed off another. If we get apple, the bigger component of the three dishes
is orange, hence orange is the larger part and the official decision. The use of diverse trees in
arbitrary woodlands reduces the risk of overfitting. It takes less time to prepare. It has a high level
of accuracy and operates efficiently on a large data set. It generates extremely precise projections
for large amounts of data. When a large amount of data is missing, it can keep up with precision.
2.3. Segmentation
Picture division is the most popular means of dividing an advanced picture into multiple portions
in computerized picture management and PC vision. The purpose of the division is to improve
and transform a picture's depiction into something more substantial and simply to analyse. It
allows the name of each point of the picture. Edge strategy, edge-based division, grouping
strategies, chart-based strategies, and so on are examples of division approaches [7]. As a rule,
OpenCV captures images and recordings in 8 bits, whole number, RGB format (0, 0, 255), for
example, (0, 0, 225) is supplied as dark blue since the blue border is going for maximum cost
255, in these lines, we adjusted it in different pixels. Creation of The RGB image inside the
picture, with each small box addressing a different image component. In real photos, the area unit
of these pixels is so small that the human eye can't tell them apart. As a result, the HSV shading
region is used and has three grids: 'tone,' 'immersion,' and 'worth.' The cost of the 'tint',
'immersion', and 'worth' region units in OpenCV are 0-179, 0-255, and 0-255, respectively. 'Tint'
refers to shading, 'immersion' to the percentage of that solitary tone mixed in with white, and
'worth' to the percentage of that singular tone merged in with the dark. The shaded element makes
the mechanism poor sensitivity to illumination variations [8,9]. It's a design for device-
independent shading representation. The HSV shading model is useful for locating certain
shading ranges, such as skin shading, chimney tone, etc.
3. IMPLEMENTATION
3.1. Data-set Preparation
Kaggle provides various data sets distribution models in which dataset distributors publish the
values in an open environment. We have used a CSV file to capture data set values, which is
well-known for record designs provided by Kaggle. They're a better option for exact values.
KNN will show lines and sections uniformly in CSV documents. In three portions, we have
divided the dataset values into two cells containing the autonomous components of fire and
smoke and the dependent variable of fire status. The dependent variable is the fire status, which
depends on the value of fire and smoke. For comparing three segments, the dataset record has
over a thousand lines of data.
It also includes testing recordings to determine the fire condition's yield. The reliant variable is
supplied into the name encoder from the datasets obtained.
3.2. Mark Encoder
Python's module aims to transform the unprocessed value into integers, allowing us to visualize
our predictive models more clearly. The third portion will be the smoky status segment (y),
represented as text design. If we run any model on string data, we will almost certainly make a
mistake. As a result, this information must be prepared for the model before we can run it. The

70
Label Encoder class converts text or string information into mathematical details. To flip the final
value, LabelEncoder is included, updates the final segment of information, and new converted
information is swapped with current information to encode the last piece. As a result, the fire
status changed into mathematical form, and the "Zero smoke and Zero fire" state is assigned 2.
Essentially, "Smoke" is assigned a value of 3, "Fire and Heavy Smoke" we have posted a value of
0, and "Substantial Fire and Heavy Smoke" is given a value of 1.
3.3. Train Test Split
The varying value changed into mathematical information; later, autonomous value and the
reliant variable divided into preparation besides testing knowledge. The Preparation values have
been used to create a frame that will be approved for testing. 0.75 percent of the material is
haphazardly classified as preparation information. In contrast, the rest of 25% is classified in
testing information. The irregular state is used to divide the dataset randomly. You can adjust the
split rate according to your preferences, but for best results, it's best to supply about 60% of the
information as train information. Train datasets X and Y will comprise 75 percent of the
preparation information. The test datasets X and Y will make up 25% of the testing information.
3.4. KNN Algorithm Implementation
For KNN calculation, We have imported Python essential libraries. We additionally used The
NumPy libraries to create clusters and networks. The usage of pandas' to browse the dataset
document. Then, at that time, two AI libraries are brought in. KN To do the K nearest neighbor,
use neighbors Classifier from sklearn. Neighbors, and precision from sklearn. Metrics for the
exactness arrangement score. K's value has been assigned arbitrarily to 3. Because there is no
one-size-fits-all approach to calculating the K value, we'd instead try a few qualities and pick the
least complicated one. A low K value may result in strange and noisy aftereffects within the
model. A higher reward for K may be acceptable, but it will be fraught with difficulties.
3.5. Arbitrary Forest Algorithm Implementation
We have been used Python essential libraries for the random forest computation and created the
NumPy libraries for the usability of clusters and networks. We have used the pandas' library to
browse the dataset in the document. Then, for the exactness order score, bringing in two AI
libraries: Random Forest Classifier from sklearn ensemble and precision for sklearn metrics. The
Random Forest uses n estimators = 10, which is the required number of trees. As a default, we
have used esteem ten. The random value is selected, but the deal must address overfitting.
3.6. Disarray Matrix
A disarray network is a perception of predicted results on a characterization issue, regardless of
whether or not the computation has effectively anticipated. The count esteems used to sum up,
the expected attributes that are correct and incorrect. When your arrangement model creates
forecasts, it gets puzzled by the disarray grid images.
There are four cases, as follows.
• True Positives (TP): These were occasions when we predicted yes (fire and smoke), and there
was indeed fire and smoke.
• True Negatives (TN): We didn't expect anything, and there won't be any fire or smoke.
• False Positives (FP): We expected fire and smoke, but there would be none.
• False Negatives (FN): Although we didn't expect it, there would be fire and smoke.

71
We have determined the testing precision for the calculation from the beneath equation:
Accuracy = (TP+TN)/(TP+TN+FP+FN)
3.7. Precision
As a result, we received a response of 75% of the datasets used to create the model for both
calculations. With the X and Y, a KNN calculation model and a Random Forest calculation will
now be used as a recurring model. Finally, accuracy in preparation is the expectation for both
computations. We have used Nearly a quarter of data values for testing the model, with the X test
being sent to the prepared model, resulting in Y foresee as a solution for the corresponding X test
found. Currently, we examined the Y test and Y anticipation. And we moved forward, expecting
the testing precision for both computations. Lastly, we have predicted the testing exactness for
both calculations using the Disarray framework.
3.8. Division
A video document from a Kaggle was downloaded and played. Later we examined the video and
identified two components: got an image. Called has no value and is destroyed; from another
video, the picture is archived. The video is then scaled, and a casing is used to cut it into an
outline. The resized image is eventually tucked away in the picture. In shading, RGB tones are
typically seen, basically based on division.
On the other hand, HSV tones are the most well-suited shading for shade-based picture division.
Following the conversion of values of one form to another from the video (outline by outline).
All are co-identified with the shading luminance in R, G, and B of RGB tones, implying
luminance we cannot separate shading data. HSV is used to separate image brightness from
shading data.
If the brightness of the picture/outline is required, chipping away at HSV makes it plainer and
more fundamental. HSV is also used in situations where color representation plays a vital role. As
a result, the RGB values are converted to HSV values outlined in the outline. For a range of fire
and smoke tones, we edge the HSV image. Then, using the HSV range shade of red and yellow,
extraction of yellow and red then blend from supplied reach to cover the fire in the picture, which
will emerge from outline to outline. The red district will be covered according to the value range,
and then the number of non-zero pixels in the red region of the image will be tallied and stored in
red. In addition, the characteristics in the yellow district will are stored.
Finally, we will cover the red-yellow mix and compute the general picture element of fire. Then
the result is placed in the fire. In addition, to veil smog in the picture, which is shown outline by
outline, smog is covered with the help of specified reach. The mixture obtained from the bank
and ask is then placed in mask7. Cover 8 hides the link, and veil 7 merges once more. So, in veil
8, all of the smoke assortments will be present, non-zero pixels also be tallied, and veil eight will
store their value in smoke. Finally, the smoke and fire esteem fed from veiling into the classifier
computation yields the yield for the specified attributes. Now it's been printed. One of the four
fire statuses in the video is delivered. Finally, cv2.imwrite is used to save the recordings, as
shown in "target.png." we cannot watch the played video remotely via cv2.imshow; otherwise,
we can play it internally. While the video is playing, you should hold it. Otherwise, the cycle will
get damaged when "q" is pushed along the way.

72
4. RESULTS AND DISCUSSIONS
4.1. Data set Preparation
Kaggle is a source for finding data values, including 997 sections of information, with 746 lines
of communication for setting the model and 251 bars for the testing constructed framework.
4.2. Train Test Split and Mark Encoder
A train test split will change the smog state over twofold integer worth, and in the framework, we
have addressed of pre-processing function in Label Encoder. Preparation good results are
obtained from X and Y trails for the two estimations. Later, we analyzed the operations on Y
trails. With the evaluation, the confusion matrix portrays the computation's right and erroneous
assumptions.
4.3. KNN Algorithm Confusion Matrix
Figure 1. KNN-Algorithm Confusion Matrix
4.4. Random Forest Algorithm Confusion Matrix
Figure 2. Random Forest Algorithm Confusion Matrix
In the above matrices, the characteristics in disarray network where the diagonals have the
genuine and expected smog state which effectively anticipated qualities are. Various
characteristics in the cross section lead to the erroneous estimates considering the way that the

73
genuine and expected smog state isn't identical. The values presented in second figure 2 are
presented as:
Accuracy = Total number of right expectations/Total number of expectations
= 249/251
= 0.992
Additionally, testing exactness is determined through KNN calculation from previous.
4.5. Yields for information video
Figure 3(a)
Figure 3(b)

74
Figure 3(c)
Figure 3(d)
Fig. 3 Results for (a) No Fire and No Smoke (b) Heavy Fire and Smog (c) Smog
(d) Fire and Maximum Smog
Henceforth from figure 3 by means of covering strategy in picture handling and AI calculations,
fire and smog is identified then interfaced for the result outline by outline.
Table 1. Accuracy of Different Classification Algorithms.
S.no Methods Achievement
1 KNN 98.54%
2 Random
Forest
99.66%
KNN calculations are executed which is providing 98.54% accurate later Random Forest
calculations are utilized providing an improvement of 99.66%. Consequently, the exactness is
enhanced.

75
5. COMPARISION WITH PREVIOUS MODELS
Fire Cooperative Research Centre, concerned with bushfires, collaborated with the Australian
government; the Australian government has produced an appealing article on the experience with
three different optoelectronic devices; the Bushfire Collaborative Research Centre started the
ongoing research to minimize bushfire incidents. This paper has evaluated the findings of the
other technologies and conceived a project comparing the effectiveness of optical sensing
devices. Mathews and others [10] created a holistic approach while assessing the information, the
testing environment, the impacts of the testing, and the assessment. [10]
The two mechanisms which are usually dependent on today are EYEfi, FireWatch and
ForestWatch.
Mechanisms examined all the above on three various forms of fires in Tumut, New South Wales,
and Otway Range, Australia, in the yr 2010. The blazes affected the study sector, the non-public
sector, and the business sector. See Figure 4.
Figure 2. Tumut Tower of Tumut, Australia [10].
Compared with previous findings, our proposed model of prior bushfire mechanism using ML
algorithms can definitely avoiding the cost and increasing the accuracy.
Table 2. Accuracy of Different Classification Algorithms.
The comparison
Image processing-based forest
fire detection
Promise of Machine Learning
Technical
Implementation
Thermal Cameras are Installed ML Algorithms
Acknowledgement Detects after the fire lit.
Early detection/Constant
monitoring
Handling
Fire indication will be received
from processing of image
Get the information by
Implementing the Captured
dataset
Explicit Fair Very Fair
Fire located Zone - Wilde
Accurate because of promise
from ML
Coverage Zone is consisted Extended Area
Base
Supposed to build the high
range tower to implement the
camera
No need of a physical existence
other than the dataset from
Satellite.
Comparison
Accuracy Achievedt
98.71% 98.54%

76
6. CONCLUSION
Finally, we conclude that we have utilized the two portrayal estimations; one is KNN
computation that is completed and provides the precision of 98.54%, second is Random Forest
yielding 99.66% of improvement and the comparisons of past technology addressed 98.71% but
it’s not considered under the early detection. From this time forward, the accuracy has extended
to practically 1.2%. Then, a picture dealing with strategies, for instance, the division utilized for
veiling, is executed to anticipate disaster issues by diagram whenever we run the video. The
outcome relies upon the four fire states unendingly layout by the graph. In the future, it should
send a caution to the Backwoods office authorities via digital techniques giving the particular
space of disaster caused at whatever point recognized in the bush locale to thwart the immense
regular effect anomaly in the natural framework.
REFERENCES
[1] Sinha, Ditipriya, Kumari, Rina; Tripathi, Sudhakar, “Semisupervised Classification Based Clustering
Approach in WSN for Forest Fire Detection.”, Wireless Personal Communications, Dec2019, Vol.
109 Issue 4, p2561-2605. 45p.
[2] Shivam Pareek, Shreya Shrivastava, Sonal Jhala, Juned A. Siddiqui, Savitanandan Patidar "IOT and
Image Processing based Forest Monitoring and Counteracting System”, 4th International Conference
on Trends in Electronics and Informatics (ICOEI), 2020.
[3] Tao Wang, Jianshuo Hu, Tingyu Ma, Jing Song "Forest fire detection system based on Fuzzy Kalman
filter", International Conference on Urban Engineering and Management Science (ICUEMS), 2020.
[4] Sarah Masoumi, Thomas C. Baum, Amir Ebrahimi, Wayne S. T. Rowe, Kamran Ghorbani,
"Reflection Measurement of Fire Over Microwave Band: A Promising Active Method for Forest Fire
Detection”, IEEE Sensors Journal (Volume: 21, Issue: 3, Feb.1, 1 2021).
[5] Jianmei Zhang, Hongqing Zhu, Pengyu Wang, Xiaofeng Ling, "ATT Squeeze U-Net: A Lightweight
Network for Forest Fire Detection and Recognition", IEEE Access (Volume: 9), January 2021.
[6] Alina-Elena Marcu, George Suciu, Elena Olteanu, Delia Miu, Alexandru Drosu, Ioana Marcu, "IOT
System for Forest Monitoring", IEEE, 42nd International Conference on Telecommunications and
Signal Processing (TSP), 2019.
[7] C Atheeq, M Munir Ahamed Rabbani, “Mutually Authenticated Key Agreement Protocol based on
Chaos Theory in Integration of Internet and MANET” International Journal of Computer
Applications in Technology, Vol. 56, No. 4, pp.309–318, 2017.
[8] Li Jie, Xiao Jiang, "Forest fire detection based on video multi-feature fusion", IEEE, 2nd IEEE
International Conference on Computer Science and Information Technology, 2009.
[9] Jing Wang, Weiguo Song, Wei Wang, Yongming Zhang, Shixing Liu, "A new algorithm for forest
fire smoke detection based on MODIS data in Heilongjiang province", IEEE, International
Conference on Remote Sensing, Environment and Transportation Engineering, 2011.
[10] Doolin, D., Sitar, N.Wireless Sensors for Wild Fire Monitoring2005San Diego, Calif, USA SmartStructure and
Material

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AUTHORS
Dr. Mohammad Mohammad is a Senior Professor and Discipline Leader in Software
Engineering, Information Technology at Melbourne Institute of Technology, Sydney
Campus and previously worked in the area of Business Applications who served in the
IT industries for 18 years, wherein he held senior management responsibilities in the
areas of Internetworking and information systems with several of government
organisations. Mohammad holds a Bachelor of Technology, Bachelor of Information
Technology (Honours), Post Graduate Certificate in University Learning & Teaching
and Master of Education (Information Technologies) degree from Australia and
completed his PhD in Information Systems at Western Sydney University. Mohammad’s main research
activities/fields include E-government, E-learning and E-Teaching, Business Information Systems,
Business Intelligence, Educational Technology, Mobile Applications, Cloud computing Information
Security Management and Cyber Security. He was in the research industry more than 8 years and published
1 Book along with 4 International Journal articles and 10 International conferences.
Aleem Mohammed has received his Master’s degree with research in Information
Technology from Melbourne Institute of Technology in 2020. His areas of research
are IoT, Artificial Intelligence, Network Security and Cyber Security. He is a research
associate in IEEE and ACS. He has published many research articles in International
Journals and Conferences and one of his research papers was Indexed by Thomson
Reuters. He has guided many students at Bachelor and Masters level. He is currently
doing research on a cancer prediction model along with the team of PhD’s on an NCI
hub platform.
Dr. C. Atheeq has received his doctorate degree in CSE department from BS Abdur
Rahman Crescent Institute of Science and Technology in 2021. His areas of research
are Mobile Ad-hoc Networks, Artificial Intelligence, Network Security. He has
published many research articles in International Journals and Conferences. He has
guided many students at Bachelor and Masters level. He also served as reviewer for
IEEE Wireless Communications. He is currently working as Assistant Professor in
the department of CSE at GITAM University, Hyderabad.

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